Foreign Aid Linked to Infrastructure and/or Pollution Abatement

This paper studies the macroeconomic effects of a permanent increase in foreign aid in a model that takes into account environmental quality. We develop a dynamic equilibrium model in which both public investment in infrastructure and environmental protection can be financed using domestic resources and international aid programs. The framework considers four scenarios for international aid: untied aid,aid fully tied to infrastructure, aid fully tied to abatement, and aid equally tied to both types of expenditures. We find that the effects of the transfers may depend on (i) the structural characteristics of the recipient country (the elasticity of substitution in production and its dependence on environment and natural resources) and on (ii) how recipient countries distribute their public expenditure. These results underscore the importance of these factors when deciding how and to what extent to tie aid to infrastructure and/or pollution abatement.

Prioritization of candidate cancer genes-an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.